Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

Sophie M. Woodward; Daniel Mork; Xiao Wu; Zhewen Hou; Danielle Braun; Francesca Dominici

doi:10.1371/journal.pgph.0002178

Abstract

Imposing stricter regulations for PM_2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM_2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM_2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM_2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM_2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m³ in long-term PM_2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual’s odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM_2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Citation: Woodward SM, Mork D, Wu X, Hou Z, Braun D, Dominici F (2023) Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States. PLOS Glob Public Health 3(8): e0002178. https://doi.org/10.1371/journal.pgph.0002178

Editor: Gautam I. Menon, Ashoka University, INDIA

Received: January 13, 2023; Accepted: June 21, 2023; Published: August 2, 2023

Copyright: © 2023 Woodward et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and code are publicly available at https://doi.org/10.7910/DVN/3ZU0AS and https://github.com/NSAPH/PM_COVID_ecoinference.

Funding: This work was made possible by support from NIH grants R01 ES024332-01A1, P50MD010428, R01 ES026217, R01 ES028033, R01 ES030616, R01 AG066793-01, R01 MD012769, P30ES000002, R01 MD016054-01A1, and T32ES007142; Health Effects Institute grant (HEI) 4953-RFA14-3/16-4; US EPA grant 83587201-0; the Climate Change Solutions Fund; Harvard University’s “Data Science Methods and Tools for Climate Change Solutions” grant; the Fernholz Foundation; and the 2020 Harvard Star-Friedman Award for Promising Scientific Research. The funding sources did not participate in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The research described in this article was conducted under contract to the HEI, an organization jointly funded by the EPA (Assistance Award No. R-83467701), and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers.

Competing interests: The authors have declared that no competing interests exist.

Introduction

It is well known that greenhouse gases (GHG) and fine particulate matter (PM_2.5) share the same emission sources (e.g. coal-fired power plants and diesel-fueled vehicles) [1, 2]. Therefore, implementing air quality control strategies in the United States will have the additional benefit of GHG emission reduction. On January 6, 2023, the EPA announced a proposal to strengthen the NAAQS for PM_2.5 to better protect communities, with a focus on vulnerable populations who may be disproportionately impacted by the effects of air pollution [3, 4]. The EPA’s proposal specifically aims to lower the primary (health-based) annual PM_2.5 standard from a level of 12 μg/m³ to a level between 9 and 10 μg/m³, taking into account the most up-to-date science. A growing body of evidence has established the association between exposure to PM_2.5 and adverse health outcomes including heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and respiratory infections, among others [1]. The impacts of PM_2.5 also extend to more recent health concerns including adverse COVID-19 outcomes based on studies both within the US [5] and worldwide [6].

The majority of the studies linking long-term PM_2.5 exposure to COVID-19 health outcomes have relied on analyses of aggregated data [7], since publicly available data on COVID-19 outcomes are, for the most part, only available at aggregate-level (e.g., Johns Hopkins Coronavirus Resource Center or New York Times COVID-19 Tracker) [8, 9]. One of the first studies published on this topic analyzed county-level data for 3,082 US counties for the period March 1 to June 18, 2020, and found that an increase of 1 μg/m³ in long-term exposure (averaged during the years 2000–2016) to PM_2.5 was significantly associated with an 11% (95% confidence interval, 6 to 17%) increase in the risk of COVID-19 death [5]. However, a major limitation of the aforementioned study is that it solely relied on county-level data [10, 11], so its conclusions cannot be interpreted as individual-level associations. Confusion between ecological associations and individual-level associations may present an ecological fallacy [12], which leads to associations detected in ecological regressions that do not exist or are in the opposite direction of true associations at the individual-level (i.e., ecological bias) [13–15]. To our knowledge, no study to date has performed an analysis of nationally representative data in the United States investigating the individual association between exposure to PM_2.5 and COVID-19 mortality.

To overcome the challenge of ecological bias, a few studies in the United States have acquired and analyzed individual-level data on COVID-19 patients within specific subgroups or geographical locations. Up to January 5, 2023, we found a few US cohort studies, in which researchers accessed the University of Cincinnati healthcare system, the US Department of Veterans Affairs national healthcare databases, and Kaiser Permanente Southern California electronic medical records (EMR), to investigate the individual-level association between air pollution and COVID-19 outcomes [16–20]. Specifically, Mendy et al. (2021) acquired data on 14, 783 COVID-19 patients diagnosed at the University of Cincinnati healthcare system and found a significant positive association between long-term PM_2.5 exposure and hospitalization among COVID-19 patients with asthma or COPD, but an insignificant association among patients without asthma or COPD [16]. Bowe et al. (2021) accessed individual-level data from the US Department of Veterans Affairs national healthcare databases and built a national cohort of 169, 102 veterans who tested positive for COVID-19. The authors found that long-term exposure to higher levels of PM_2.5 was significantly associated with an increased risk of COVID-19 hospitalization [17]. Chen et al. (2022), Jerrett et al. (2023), and Sidell et al. (2022), analyzed COVID-19 outcomes among 4.6 million members of the Kaiser Permanente Southern California and found that short- and long-term PM_2.5 exposure was significantly associated with increased risk of COVID-19 disease incidence, severity, and mortality [18–20]. These are powerful studies, but they include highly selective study populations and rely on health records that are not publicly available. Three additional studies worldwide have estimated associations between exposure to PM_2.5 and COVID-19 outcomes using finer spatial units [21–23], the first using data at the census tract-level in Colorado, US, and the second and third using data at the level of small areas designed to be of similar population size in England and Scotland, respectively. These three studies implemented Bayesian hierarchical models with spatially-structured random effects. Although these studies may mitigate ecological bias by considering small spatial units, they do not harmonize data at different levels of spatial aggregation and do not incorporate any individual-level data. In this paper, we present the first and only epidemiological study in the US that adjusts for ecological bias and provides a more rigorous estimate of the individual-level association between long-term exposure to PM_2.5 and COVID-19 mortality.

Materials and methods

We begin by briefly summarizing our contributions. First, we harmonize and link nationally-representative US data from several publicly available sources at different levels of spatial aggregation. More specifically we link and harmonize data at the county-, gridded-, and individual-level to create a dataset containing the following: 1) COVID-19 deaths (county-level) which is our outcome; 2) exposure to air pollution (PM_2.5) (census tract-level); 3) potential confounders including demographics and socioeconomic variables (individual-level); and 4) other county-level variables as potential confounders. Table 1 summarizes the data, the data sources, and their spatial resolution. Second, we implement a Bayesian hierarchical model to draw inference on individual-level associations between long-term PM_2.5 exposure and COVID-19 mortality by combining information across individual-level, census tract-level, and county-level data [15]. Third, we update our previously constructed data repository for the period March 1 to June 18, 2020 [5], with data up to December 1, 2020, before the Emergency Use Authorization (EUA) for the Pfizer-BioNTech COVID-19 Vaccine, and implement both the proposed Bayesian hierarchical models and ecological regression models on the updated data. Fourth, we conduct extensive sensitivity analyses to examine the sensitivity of our results to different specifications of the statistical models. Finally, we make all data and code publicly available at https://doi.org/10.7910/DVN/3ZU0AS and https://github.com/NSAPH/PM_COVID_ecoinference, so other researchers can reproduce our analyses or apply a similar modeling framework to their data.

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Table 1. Details of the data analyzed using our proposed Bayesian hierarchical model for combining sources at multiple spatial resolutions.

https://doi.org/10.1371/journal.pgph.0002178.t001

Exposure data

Our primary exposure of interest is fine particulate matter (PM_2.5). Consistent with Wu et al. [5], we use PM_2.5 exposure data from an extensively cross-validated exposure prediction model [26], which predicts monthly PM_2.5 exposure levels by fusing PM_2.5 measures from three different sources: ground-based monitors, GEOS-Chem chemical transport models (CTM), and satellite observations at 0.01° × 0.01° grid resolution across the entire contiguous United States. We obtain long-term temporally averaged PM_2.5 (2000–2016) by averaging the monthly concentrations. We used zonal statistics to aggregate the 0.01° × 0.01° gridded estimates of PM_2.5 concentration to the census tract-level. To adjust for ecological bias, we additionally estimate the population-weighted variance of PM_2.5 within each county. To do so, we first obtain the population sizes of all census tracts (which are much finer than counties), and then calculate the weighted variance of PM_2.5 concentration in each county, incorporating census tract population sizes as the weights.

Mortality data

We obtain and aggregate COVID-19 daily death reports for each county in the United States from the Center for Systems Science and Engineering at Johns Hopkins University for the period March 1 to December 1, 2020 (prior to the EUA for the Pfizer-BioNTech COVID-19 Vaccine) [8]. It is important to note that mortality across states may have been affected by state-level policies and reporting [27].

County-level confounders

We treat long-term exposure to NO₂ and O₃ as county-level potential confounders. We use well-validated prediction models to estimate exposure to NO₂ and O₃ at the daily level from 2000 to 2016 at 1 km² grids across the entire contiguous United States [24, 25]. We obtain long-term temporally averaged NO₂ and O₃ concentrations (from 2000 to 2016) by aggregating these concentrations to counties and averaging daily concentrations across these years. In this paper, we focus on PM_2.5 exposure but similar methods could be used to extend the model to account for the within-county variability of NO₂ and O₃ exposure as well.

In addition to long-term exposure to NO₂ and O₃, we acquire another eight county-level variables to account for possible confounding bias at the county-level. Specifically, we consider two behavioral risk factor variables from the Robert Wood Johnson Foundation’s 2020 County Health Rankings: the proportion of residents who are obese and the proportion of residents who are current smokers; and four meteorological variables from Gridmet via Google Earth Engine: average daily temperature and relative humidity for summer (June-September) and winter (December-February) for each county [28, 29]. Finally, we obtain county-level population density estimates from the 2019 ACS, and the county-level number of hospital beds in 2019 from Homeland Infrastructure Foundation-Level Data.

Public Use Microdata Sample (PUMS) data

We accessed a nationally representative data set with individual-level covariate data from the 5-Year Public Use Microdata Area (PUMA)-level PUMS files for the years 2015–2019, from the US Census Bureau’s ACS. The PUMS files are a set of records from individual people or housing units. PUMS files covering a five-year period contain data on approximately 5% of the United States population. The most detailed unit of geography in the PUMS files is the Public Use Microdata Area, which we link to US counties. For more details see [30] and S1 File. In our study, we consider eight individual-level demographic, social, and economic covariates from US Census Bureau’s ACS. These include six categorical covariates: sex, age, race, graduation status, house ownership, and poverty status; and two continuous covariates: household income and house value. To account for differences in COVID-19 mortality risks across age, race, and socioeconomic groups, we define 96 strata characterized by the joint distribution of the six individual-level categorical variables. Each stratum is determined by a unique combination of the values of categorical covariates: male/female, age 0–39/40+, White/Black/other race, graduated/not graduated from high school, owns/does not own house, and in poverty/not in poverty.

Statistical methods

Bayesian hierarchical model.

We define a Bayesian hierarchical model to estimate the individual-level association between long-term exposure to PM_2.5 and COVID-19 deaths. This model builds upon the work by Jackson et al. (2006, 2008) with the goal of making inferences about individual-level relationships by combining aggregate- and individual-level data [15, 31]. The coefficients of this Bayesian hierarchical model are consistent with an underlying individual-level logistic model regressing the (unobserved) binary indicator of the outcome, COVID-19 death, for each individual to other independent variables, only requiring area-level death counts as the dependent variables.

To illustrate the mathematical details of our hierarchical model, we first introduce the underlying individual-level logistic regression model. Let j = 1, …, n_i index the individuals in county i = 1, …, N. The binary indicator, y_ij, of COVID-19 related death for an individual j in county i can be modeled as (1) where p_ij is the probability of COVID-19 death; is the state-level random intercept for the state corresponding to county i; x_i is a vector of county-level covariates for county i with linear effect coefficients α; v_ij is a vector of individual-level continuous covariates (e.g., PM_2.5, log household income, log house value); I_ijk is an indicator that is equal to 1 if the individual j in county i belongs to stratum k; and γ_k is the effect for stratum k. We include state-specific random intercepts to control for differences in state-level policies and reporting practices. As described in the PUMS data section, we define 96 strata characterized by the joint distribution of the six individual-level categorical variables sex, age, race, graduation status, house ownership, and poverty status. Note that PM_2.5 exposure is estimated on a high-resolution spatial grid and aggregated to the census tract-level, but we considered it here as an individual-level continuous variable under the assumption that individuals within the same census tract have similar long-term PM_2.5 exposure. Using estimated PM_2.5 at the census tract-level enables us to incorporate information about its within-county variance in the model.

The individual-level outcome data on COVID-19 deaths y_ij, individual-level continuous covariates v_ij, and individual-level strata indicators I_ijk are unobserved, which prevents us from fitting the logistic regression model described in Eq 1 directly. However, by harnessing our individual-level data to estimate the within-county distribution of v_ij, we are able to fit a hierarchical model to estimate the individual-level association between long-term PM_2.5 exposure and COVID-19 mortality. Although for certain highly selective patient groups we might access individual-level death records via EMR, in general y_ij is not publicly available for most US counties. Instead, we observe county-level COVID-19 deaths, , for the whole contiguous United States. To harness individual-level covariate information into a statistical model with an aggregated outcome, we propose the hierarchical model, where p_i defines the marginal probability of COVID-19 death for county i, q_ik is the probability of COVID-19 death for an individual in county i who belongs to stratum k, is the proportion of individuals in county i belonging to stratum k estimated based on the PUMS data sample, and g_i denotes the joint density of the individual-level continuous covariates (PM_2.5, log household income, log house value) within county i, which we assume is multivariate normal with estimated mean vector v_i and estimated covariance matrix . Within each county, we assume that PM_2.5 is uncorrelated with household income and house value, hence four entries of equal 0. Table 2 describes the notation in detail.

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Table 2. Descriptions of the notation for parameters and data used in the Bayesian hierarchical model.

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Under the assumption that individual-level continuous covariates are normally distributed within each county, q_ik can be approximated as (2) where , based on the probit approximation of the logit link function [15]. We log-transformed house value and household income to satisfy the assumption of normality and visually checked this assumption using histograms of log house value, log household income, and PM_2.5 in each county. The log-likelihood of the proposed hierarchical model is (3) An important feature of this model is that by maximizing the likelihood function described above we can make inferences on parameters in the individual-level logistic regression model. In other words, with this hierarchical model, we can infer individual-level association, even though only county-level outcomes (y_i) on COVID-19 deaths are available.

Bayesian model specification and computation.

We implement a Bayesian hierarchical model via R statistical software (version 3.5.1) using package rStan [32, 33]. For each of the regression coefficient parameters {α, β}, except for female and age ≥ 40, we choose Gaussian priors to reflect a 95% prior belief that each odds ratio is between 1/5 and 5. To reduce bias related to weak identifiability [34], we choose Gaussian priors for the female coefficient and age ≥ 40 coefficient based on Provisional COVID-19 Deaths by Sex and Age from the National Center for Health Statistics [35]. For the state-specific intercept , we assign a hierarchical prior allowing for a random state-specific baseline risk of COVID-19 death. See S2 File for complete details on prior selection.

We incorporate K = 96 strata defined by six individual-level categorical covariates in our statistical model. To avoid identifiability issues and reduce the number of parameters, we assume additive effects of individual-level categorical covariates. Under this additivity assumption, each stratum k can be represented by a unique combination of I_k1, …, I_k7, where I_kh is an indicator for whether an individual in strata k belongs to subgroup h ∈ {1, …, 7}. Each subgroup is defined by the following individual-level categorical covariates: poverty, high school graduation, house ownership, age ≥ 40, female, Black, and other race respectively (note that the race variable is defined by two indicators since it contains three levels (White/Black/other race)). This allows us to represent each γ_k as a unique combination of parameters ω_h where . As an example, the stratum corresponding to people in poverty, who had graduated from high school, did not own their house, are younger than 40, male, and identify as “other race” (not Black or White) would be attributed an individual-level effect of γ_k = ω₁ + ω₂ + ω₇.

We used rStan to fit the model. We ran four chains, a total of 4,000 iterations each, with the first 2,000 iterations discarded as burn-in. All chains showed evidence of convergence after burn-in ( for each parameter of concern) and produced similar coefficient estimates, which were combined to produce the final results [36].

Additional analyses.

Along with the main analysis, we conduct three additional analyses to make comparisons that allow us to quantify ecological bias. First, we fit an ecological regression analysis to our dataset to compare alongside our main analysis. Second, we apply our Bayesian hierarchical model to a subset of the data extending only until June 18, 2020, to match the data used in the previous published ecological regression [5]. As Wu et. al. (2020) utilized a different modeling approach for the reduced dataset, we also conduct an ecological regression analysis to the dataset extending through June 18, 2020 to quantify ecological bias.

Sensitivity analyses.

To evaluate the robustness of the estimated odds ratios for PM_2.5 to various modeling specifications, we conduct a series of sensitivity analyses. First, we account for four categories in age (0–17, 18–39, 40–64, 65+) instead of two (0–39, 40+) to more accurately adjust for variations in COVID-19 mortality by age. Second, we use county-level deaths and the PUMS data sample to estimate fixed values for the strata specific risk coefficients, γ_k. Using fixed values as opposed to estimating the strata-specific coefficients allows us to consider potential bias in the categorical offsets related to weak identifiability. Third, we include PM_2.5 as a county-level covariate in the Bayesian hierarchical model, similar to NO₂ and O₃, to determine the robustness of the estimated ORs for PM_2.5 if the within-county variability of the exposure was not taken into account. We run all combinations of the proposed sensitivity analyses and compare to our main analysis.

Results

Fig 1 shows: 1) county-level 17-year average PM_2.5 concentrations (2000–2016) in the United States in μg/m³; 2) estimated population-weighted variance of the census tract-level PM_2.5 concentrations (2000–2016) within each county; and 3) county-level number of COVID-19 deaths per 100,000 individuals in the United States for the period March 1 to December 1, 2020. These maps show higher PM_2.5 levels in the southwest and southeast; higher PM_2.5 variances in the west; and higher COVID-19 death rates in the southwest, southeast and mid-west. In counties with higher exposure variances, county-level average exposure to PM_2.5 is less representative of the census tract aggregated exposure. Indeed, it is well known that as the ratios of within-county to between-county variability of exposure and confounders increase, ecological bias increases [15, 31].

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Fig 1. Maps showing (a) county-level 17-year average of PM_2.5 concentrations (2000–2016) in μg/m³, (b) estimated population-weighted variance of the census tract-level PM_2.5 concentrations (2000–2016) within each county, and (c) county-level COVID-19 death counts per 100,000 individuals in the United States up to December 1, 2020.

Estimated variances of PM_2.5 are equal to zero in 6% of counties because they only contain one census tract. Base geography layers retrieved from U.S. Census TIGER/Line shapefiles: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.

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Table 3 summarizes the characteristics of the study cohort. In Table 4, we report the estimated regression coefficient for each of the covariates included in our main analysis. In our main analysis, we consider a study period of March 1 to December 1, 2020, and account for ecological and confounding bias by incorporating individual-level covariate data from PUMS and census tract-level exposure data for PM_2.5 in the proposed Bayesian hierarchical model. We found that an increase of 1 μg/m³ in long-term average PM_2.5 is associated with an increase in an individual’s odds of COVID-19 death by 3.3% (95% credible interval (CI): 2.8% to 3.8%), after adjusting for both ecological and confounding bias. Importantly, we also found that county-level nitrogen dioxide and ozone (NO₂ and O₃), population density, rate of hospital beds, average summer and winter temperature, average summer and winter relative humidity, obesity, smoking status, and individual-level poverty status, high school graduation status, house ownership, age, sex, race, household income, and house value are significantly associated with COVID-19 mortality rate.

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Table 3. Characteristics of the study cohort.

For census tract-level data, the mean and standard deviation across all US census tracts is calculated weighted by the populations in the census tracts. For county-level data, the mean and standard deviation across all US counties is calculated. For individual-level categorical data, the percent of individuals in each category is calculated. For individual-level continuous data, the mean and standard deviation across all individuals in the Public Use Microdata Sample is calculated, using accompanying sampling weights provided by American Community Survey (ACS). Other race denotes races other than White and Black.

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Table 4. COVID-19 mortality odds ratios and 95% credible intervals (CI) for all covariates, accounting for ecological and individual-level confounding bias using our proposed Bayesian hierarchical model.

Odds ratios for individual-level categorical variables can be interpreted relative to the following baseline levels: not in poverty, not graduated from high school, does not own house, age 0−39, male, and White race. For population density, Q denotes quintile with coefficients relative to Q1. Other race denotes races other than White and Black.

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In Table 5, we report the estimated OR and 95% CI for PM_2.5 under the following three additional analyses described in the Methods section: 1) ecological regression analysis for the whole study period (March 1 to December 1, 2020) not accounting for ecological bias (i.e., using county-level data only); 2) analysis accounting for ecological bias, but for a shorter time period (March 1 to June 18, 2020, to align with the previous analysis [5]); 3) ecological regression analysis not accounting for ecological bias for the time period of March 1 to June 18, 2020. We compare the results from these three additional analyses with the results from the main analysis (the estimated coefficients for the main analysis are shown in Table 4). There are three important findings. First, the estimated OR for PM_2.5 for the shorter period (up to June 18, 2020) is larger than the estimated OR for the longer study period (up to December 1, 2020). Second, adjusting for ecological bias by incorporating individual-level confounders and census tract-level PM_2.5 reduces the estimated OR. However, the smaller OR reported in the main analysis is mostly due to updating the data up to December 2020 and less due to the adjustment for ecological bias. Overall, all four estimated ORs show statistically significant positive county-level/individual-level associations between long-term exposure to PM_2.5 and COVID-19 mortality.

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Table 5. Summary of COVID-19 mortality odds ratios and 95% credible intervals (CI) for a 1 μg/m³ increase in PM_2.5 from additional analyses.

The first row represents our main analysis with individual-level confounders and census tract-level PM_2.5 accounting for ecological and confounding bias (up to December 1, 2020). The second, third, and fourth rows represent the three additional analyses respectively: ecological regression with solely county-level data (up to December 1, 2020); Bayesian hierarchical regression with individual-level confounders and census tract-level PM_2.5; and ecological regression with solely county-level data (up to June 18, 2020).

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We conduct several sensitivity analyses, where we compare the results from our main analysis with results under seven additional model specifications and implementations described in the Methods section. We find results from our sensitivity analyses are overall consistent with results from the main analysis. In particular, we find statistically significant positive associations between long-term exposure to PM_2.5 and the odds of COVID 19 death, regardless of the model specification (see Table 6 for the results).

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Table 6. Summary of COVID-19 mortality odds ratios (OR) and 95% credible intervals (CI) for a 1 μg/m³ increase in PM_2.5 from sensitivity analyses based on 1) changing the number of age groups used for stratification, 2) using fixed versus estimated offsets for strata-specific odds ratios, and 3) estimating PM_2.5 aggregated to the county-level or census tract-level.

The first row represents our main analysis.

https://doi.org/10.1371/journal.pgph.0002178.t006

Discussion

Our work presents the first study using nationally representative and publicly available data in the United States to investigate relationships between air pollution exposure and COVID-19 mortality and make inference at the individual-level. Our statistical model adjusts for a range of socioeconomic, demographic, meteorological, behavioral, and health-related confounders. Compared to an ecological regression model, our Bayesian hierarchical model adjusts for ecological bias by incorporating individual-level data and produces estimates that continue to indicate strong evidence of a harmful relationship between increased PM_2.5 exposure and odds of COVID-19 mortality. Our results are robust under a variety of assumptions considered in sensitivity analyses.

We found that when we analyze the data for the longer study period (March 1 to December 1, 2020) the estimated OR is smaller than when we analyze the data for a shorter period (March 1 to June 18, 2020). The smaller association in the longer period compared to the shorter period may be due to many reasons, including non-pharmaceutical interventions, changes in mobility and lifestyle, and other factors that we did not control for in our analysis [20]. It should also be noted that in addition to having higher pollution levels, larger cities tend to have stronger international connections, which facilitated spread of the virus during the early stages of the pandemic [6]. Adjusting for population density alone may not have fully accounted for this phenomenon.

Although the exact mechanism explaining a causal relationship between higher levels of air pollution exposure and increased COVID-19 mortality remains unclear, previous work studied the potential biological links between air pollution and COVID-19 mortality. First, COVID-19 spread occurs via airborne particles and droplets, and PM_2.5 can create a suitable environment, which increases the distance of COVID-19 transmission [37–41]. Second, PM_2.5 can induce cellular inflammation, and exposure to such particles can exacerbate the susceptibility and severity of symptoms among COVID-19 patients [37, 42–45]. Third, it is also found that exposure to air pollution may suppress early immune responses to the infection, which leads to later exacerbation in inflammation and worse prognosis [46–51]. For example, long-term exposure to PM_2.5 and NO₂ leads to the overexpression of the angiotensin converting enzyme 2 (ACE-2), and this overexpression relates with the severity in COVID-19 patients [37, 52]. We maintain that our results do not imply a causal effect of air pollution, but existing work suggests that this pathway could exist.

Several studies have examined the association between COVID-19 outcomes and air pollution, including ecological regressions in different countries [5, 53–60], cohort studies including individual-level data for specific populations around the world [17, 61–71], and Bayesian hierarchical models that attempt to overcome the clustered nature of the data [21, 22]. In addition, literature reviews have been published on the topic [6, 72–77] as well as a meta-analysis [78]. Most of these studies reported evidence of a statistically significant association between exposure to air pollution and COVID-19 incidence and mortality risk. However, to our knowledge, none of these studies leverage a large and nationally representative sample of individual-level confounders and census tract-level PM_2.5 to estimate the association between exposure to air pollution and COVID-19 mortality in the contiguous United States accounting for both confounding and ecological bias.

In this nationwide study, we combine county-level, census tract-level and individual-level information from a wide range of data sources to infer individual-level associations between long-term exposure to PM_2.5 and the odds of COVID-19 death adjusting for potential confounding and ecological bias. Compared with an ecological regression analysis which only relies on county-level data of these variables, our analysis quantifies and adjusts for ecological bias by incorporating a nationally representative sample of demographic, social, and economic variables from the 2015–2019 American Community Survey (ACS) Public Use Microdata Sample (PUMS). Due to data privacy constraints, it is increasingly challenging to collect a large amount of nationally representative individual-level data relevant to COVID-19 outcomes and make it publicly available in a timely manner [79]. Our proposed Bayesian method overcomes these constraints in a novel way. It allows us to obtain unbiased estimates of individual-level associations when the COVID-19 outcome data is only available at the area-level by incorporating individual-level and finer spatial resolution data to account for within-area variations of both exposures and potential confounders. The approach implemented in this paper and the associated software can be applied to infer individual-level associations in any analysis that has access to individual-level confounders but only aggregate-level health data.

We have chosen to restrict our study period from March 1 to December 1, 2020. Extending our study to more recent data—and therefore post-vaccination—requires careful consideration due to a host of additional confounding factors. First, it is well documented that vaccines are highly effective at preventing hospitalizations and death [80–83]. However, controlling for vaccination rates is challenging because of the complex interaction between time and individual vaccination status as different groups became eligible to receive the COVID-19 vaccine at different times. Second, urban population centers tended to have higher vaccination rates compared to rural settings [84–86], which may serve to lower COVID-19 mortality, but urban areas are also associated with increased levels of air pollution [87], which would have the opposite relationship with COVID-19 mortality if our results hold. Hence, without properly controlling for individual vaccination status we risk biasing the results of the analysis using data from post-vaccination periods.

There are still many limitations in our study, which we hope can be resolved or improved in the future. First, within-county variances of PM_2.5 are estimated using gridded-level (0.01° × 0.01°) estimates, aggregated to the census tract-level and weighting by census tract population. While the estimated association of PM_2.5 and odds of COVID-19 mortality applies to the individual, we only have estimates of PM_2.5 at the census tract-level and not at the individual-level. However, while this is a limitation, we argue that the assumption that all individuals within the same census tract have the same long-term average exposure to PM_2.5 is reasonable. Indeed, Currie et al. (2020) report that almost all variation in individual-level PM_2.5 exposure can be explained by census tract characteristics [88]. Second, we make the strong assumption that the joint distribution of PM_2.5 exposure, log household income, and log house value is multivariate normal within each county. Moreover, estimating the correlation of PM_2.5 exposure with log household income and log house value is difficult, since individual-level PM_2.5 exposures are not available, forcing us to make the unlikely assumption that PM_2.5 exposure is uncorrelated with the other two variables within each county. Third, there exists some temporal misalignment in the exposure and confounders. However, considering that this is a cross-sectional analysis and exposure is averaged over 17 years, we expect that our results would be robust to the inclusion of more recent data. Fourth, although we were able to infer individual-level associations accounting for several individual-level and county-level confounders, results could still be biased due to unmeasured confounding. Our study relies only on publicly available data, which contains limited information on individual-level risk factors. However, we note that a recent study relating air pollution exposure with all-cause, respiratory, and circulatory hospital admissions found results to be robust to the exclusion of numerous individual-level risk factors including smoking, body mass index, and pre-existing health conditions [89]. Additionally, a COVID-19 cohort study relating PM_2.5 exposure and disease-related hospitalization risk did not find sensitivity to the inclusion of individual-level risk factors, including body mass index, cardiovascular disease, chronic obstructive pulmonary disease, diabetes, and other clinical factors [17]. Fifth, although we conduct sensitivity analyses, it remains possible that results are biased due to model misspecification. Sixth, gridded exposure estimates of PM_2.5 concentrations are obtained from an exposure prediction model, and in this analysis, we ignore its associated statistical uncertainty. Finally, we were primarily interested in PM_2.5 in this analysis, but our model could be extended to include within-area variability of NO₂, O₃, and other pertinent exposures in future work.

Considering how modifiable environmental factors alter the severity and mortality of COVID-19, among other heath outcomes, is key to helping guide public policies to reduce the negative impact of an epidemiological outbreak. This work provides updated evidence regarding the association between long-term exposure to PM_2.5 and an individual’s odds of COVID-19 mortality in the United States. Our results are derived from population-level COVID-19 outcomes, individual-level confounder data from a large national sample of 15,947,624 individuals and 7,613,443 households, and high-resolution air pollution exposures predicted from well-validated models. In line with the findings from previous studies, our analysis reveals a statistically significant, albeit small, positive correlation between long-term exposure to PM_2.5 and individual-level COVID-19 mortality. This discovery holds immense significance for public health, considering the cumulative impact of air pollution exposure on a large population. Combined with other work indicating the harmful impacts of PM_2.5 exposure on health, we refocus our attention to the NAAQS for PM_2.5 and related environmental policies. In particular, our results suggest that prior to the EUA for the Pfizer-BioNTech COVID-19 Vaccine, lowering the long-term annual average NAAQS for PM_2.5 from 12μg/m³ to follow the World Health Organization recommendation of 5μg/m³ [90] is associated with a reduction in an individual’s odds of COVID-19 mortality by 20.0%. Since GHG and PM_2.5 share the same emissions sources, implementing stricter regulations for PM_2.5 will not only lead to enormous public health benefits but also to reductions of GHG, thereby mitigating additional damaging effects and enormous costs related to climate change.

Supporting information

S1 File. Public use microdata sample.

https://doi.org/10.1371/journal.pgph.0002178.s001

(PDF)

S2 File. Prior selection.

https://doi.org/10.1371/journal.pgph.0002178.s002

(PDF)

Acknowledgments

The computations in this paper were run on the FASRC Computing Cluster supported by the FAS Division of Science, Research Computing Group at Harvard University.

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Figures

Abstract

Introduction

Materials and methods

Exposure data

Mortality data

County-level confounders

Public Use Microdata Sample (PUMS) data

Statistical methods

Bayesian hierarchical model.

Bayesian model specification and computation.

Additional analyses.

Sensitivity analyses.

Results

Discussion

Supporting information

S1 File. Public use microdata sample.

S2 File. Prior selection.

Acknowledgments

References